Overview

Dataset statistics

Number of variables17
Number of observations17379
Missing cells17379
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory136.0 B

Variable types

NUM10
CAT3
BOOL3
UNSUPPORTED1

Warnings

dteday has a high cardinality: 731 distinct values High cardinality
atemp is highly correlated with tempHigh correlation
temp is highly correlated with atempHigh correlation
cnt is highly correlated with registeredHigh correlation
registered is highly correlated with cntHigh correlation
instant has 17379 (100.0%) missing values Missing
dteday is uniformly distributed Uniform
instant is an unsupported type, check if it needs cleaning or further analysis Unsupported
hr has 726 (4.2%) zeros Zeros
weekday has 2502 (14.4%) zeros Zeros
windspeed has 2180 (12.5%) zeros Zeros
casual has 1581 (9.1%) zeros Zeros

Reproduction

Analysis started2020-11-19 18:57:31.040082
Analysis finished2020-11-19 18:57:44.025302
Duration12.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

instant
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing17379
Missing (%)100.0%
Memory size135.9 KiB

dteday
Categorical

HIGH CARDINALITY
UNIFORM

Distinct731
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
2011-12-31
 
24
2012-07-30
 
24
2012-03-14
 
24
2011-08-03
 
24
2012-02-08
 
24
Other values (726)
17259 
ValueCountFrequency (%) 
2011-12-31240.1%
 
2012-07-30240.1%
 
2012-03-14240.1%
 
2011-08-03240.1%
 
2012-02-08240.1%
 
2011-04-26240.1%
 
2011-10-16240.1%
 
2012-03-27240.1%
 
2011-10-27240.1%
 
2011-08-18240.1%
 
Other values (721)1713998.6%
 
2020-11-19T18:57:44.103278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-11-19T18:57:44.224556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
3
4496 
2
4409 
1
4242 
4
4232 
ValueCountFrequency (%) 
3449625.9%
 
2440925.4%
 
1424224.4%
 
4423224.4%
 
2020-11-19T18:57:44.328450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-19T18:57:44.401886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:44.485772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

yr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
1
8734 
0
8645 
ValueCountFrequency (%) 
1873450.3%
 
0864549.7%
 
2020-11-19T18:57:44.554080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

mnth
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.537775476
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size135.8 KiB
2020-11-19T18:57:44.619103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.438775714
Coefficient of variation (CV)0.5259855935
Kurtosis-1.201878197
Mean6.537775476
Median Absolute Deviation (MAD)3
Skewness-0.009253248383
Sum113620
Variance11.82517841
MonotocityNot monotonic
2020-11-19T18:57:44.712580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
714888.6%
 
514888.6%
 
1214838.5%
 
814758.5%
 
314738.5%
 
1014518.3%
 
614408.3%
 
1114378.3%
 
914378.3%
 
414378.3%
 
Other values (2)277015.9%
 
ValueCountFrequency (%) 
114298.2%
 
213417.7%
 
314738.5%
 
414378.3%
 
514888.6%
 
ValueCountFrequency (%) 
1214838.5%
 
1114378.3%
 
1014518.3%
 
914378.3%
 
814758.5%
 

hr
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.54675183
Minimum0
Maximum23
Zeros726
Zeros (%)4.2%
Memory size135.8 KiB
2020-11-19T18:57:44.914577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.914405095
Coefficient of variation (CV)0.5988181957
Kurtosis-1.198020588
Mean11.54675183
Median Absolute Deviation (MAD)6
Skewness-0.01067990952
Sum200671
Variance47.80899782
MonotocityNot monotonic
2020-11-19T18:57:45.032357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
167304.2%
 
177304.2%
 
157294.2%
 
137294.2%
 
147294.2%
 
227284.2%
 
187284.2%
 
197284.2%
 
207284.2%
 
217284.2%
 
Other values (14)1009258.1%
 
ValueCountFrequency (%) 
07264.2%
 
17244.2%
 
27154.1%
 
36974.0%
 
46974.0%
 
ValueCountFrequency (%) 
237284.2%
 
227284.2%
 
217284.2%
 
207284.2%
 
197284.2%
 

holiday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
0
16879 
1
 
500
ValueCountFrequency (%) 
01687997.1%
 
15002.9%
 
2020-11-19T18:57:45.120812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.003682605
Minimum0
Maximum6
Zeros2502
Zeros (%)14.4%
Memory size135.8 KiB
2020-11-19T18:57:45.191668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.005771456
Coefficient of variation (CV)0.6677707733
Kurtosis-1.255996891
Mean3.003682605
Median Absolute Deviation (MAD)2
Skewness-0.002998221376
Sum52201
Variance4.023119134
MonotocityNot monotonic
2020-11-19T18:57:45.300468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6251214.5%
 
0250214.4%
 
5248714.3%
 
1247914.3%
 
3247514.2%
 
4247114.2%
 
2245314.1%
 
ValueCountFrequency (%) 
0250214.4%
 
1247914.3%
 
2245314.1%
 
3247514.2%
 
4247114.2%
 
ValueCountFrequency (%) 
6251214.5%
 
5248714.3%
 
4247114.2%
 
3247514.2%
 
2245314.1%
 

workingday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
1
11865 
0
5514 
ValueCountFrequency (%) 
11186568.3%
 
0551431.7%
 
2020-11-19T18:57:45.386873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weathersit
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.8 KiB
1
11413 
2
4544 
3
1419 
4
 
3
ValueCountFrequency (%) 
11141365.7%
 
2454426.1%
 
314198.2%
 
43< 0.1%
 
2020-11-19T18:57:45.470955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-19T18:57:45.552323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:45.642673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4969871684
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Memory size135.8 KiB
2020-11-19T18:57:45.769396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.1925561212
Coefficient of variation (CV)0.3874468668
Kurtosis-0.9418442041
Mean0.4969871684
Median Absolute Deviation (MAD)0.16
Skewness-0.006020883348
Sum8637.14
Variance0.03707785983
MonotocityNot monotonic
2020-11-19T18:57:45.919367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.627264.2%
 
0.666934.0%
 
0.646924.0%
 
0.76904.0%
 
0.66753.9%
 
0.366713.9%
 
0.346453.7%
 
0.36413.7%
 
0.46143.5%
 
0.326113.5%
 
Other values (40)1072161.7%
 
ValueCountFrequency (%) 
0.02170.1%
 
0.04160.1%
 
0.06160.1%
 
0.08170.1%
 
0.1510.3%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.981< 0.1%
 
0.96160.1%
 
0.94170.1%
 
0.92490.3%
 

atemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4757751021
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Memory size135.8 KiB
2020-11-19T18:57:46.075817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2121
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.1718502156
Coefficient of variation (CV)0.3612005228
Kurtosis-0.8454118948
Mean0.4757751021
Median Absolute Deviation (MAD)0.1364
Skewness-0.09042885856
Sum8268.4955
Variance0.02953249661
MonotocityNot monotonic
2020-11-19T18:57:46.216801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.62129885.7%
 
0.51526183.6%
 
0.40916143.5%
 
0.33336003.5%
 
0.66675933.4%
 
0.60615883.4%
 
0.53035793.3%
 
0.55753.3%
 
0.45455593.2%
 
0.3035493.2%
 
Other values (55)1111664.0%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.01524< 0.1%
 
0.03038< 0.1%
 
0.045590.1%
 
0.0606140.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.98482< 0.1%
 
0.95451< 0.1%
 
0.92425< 0.1%
 
0.90915< 0.1%
 

hum
Real number (ℝ≥0)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6272288394
Minimum0
Maximum1
Zeros22
Zeros (%)0.1%
Memory size135.8 KiB
2020-11-19T18:57:46.364266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.78
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1929298341
Coefficient of variation (CV)0.3075908216
Kurtosis-0.8261167359
Mean0.6272288394
Median Absolute Deviation (MAD)0.15
Skewness-0.1112871494
Sum10900.61
Variance0.03722192087
MonotocityNot monotonic
2020-11-19T18:57:46.507989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.886573.8%
 
0.836303.6%
 
0.945603.2%
 
0.874882.8%
 
0.74302.5%
 
0.663882.2%
 
0.653872.2%
 
0.693592.1%
 
0.553522.0%
 
0.743412.0%
 
Other values (79)1278773.6%
 
ValueCountFrequency (%) 
0220.1%
 
0.081< 0.1%
 
0.11< 0.1%
 
0.121< 0.1%
 
0.131< 0.1%
 
ValueCountFrequency (%) 
12701.6%
 
0.971< 0.1%
 
0.963< 0.1%
 
0.945603.2%
 
0.933311.9%
 

windspeed
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1900976063
Minimum0
Maximum0.8507
Zeros2180
Zeros (%)12.5%
Memory size135.8 KiB
2020-11-19T18:57:46.642365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.1223402286
Coefficient of variation (CV)0.6435653291
Kurtosis0.5908204107
Mean0.1900976063
Median Absolute Deviation (MAD)0.0895
Skewness0.5749052035
Sum3303.7063
Variance0.01496713153
MonotocityNot monotonic
2020-11-19T18:57:46.751895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
0218012.5%
 
0.1343173810.0%
 
0.164216959.8%
 
0.19416579.5%
 
0.104516179.3%
 
0.223915138.7%
 
0.089614258.2%
 
0.253712957.5%
 
0.283610486.0%
 
0.29858084.6%
 
Other values (20)240313.8%
 
ValueCountFrequency (%) 
0218012.5%
 
0.089614258.2%
 
0.104516179.3%
 
0.1343173810.0%
 
0.164216959.8%
 
ValueCountFrequency (%) 
0.85072< 0.1%
 
0.83581< 0.1%
 
0.8062< 0.1%
 
0.77611< 0.1%
 
0.74632< 0.1%
 

casual
Real number (ℝ≥0)

ZEROS

Distinct322
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.67621842
Minimum0
Maximum367
Zeros1581
Zeros (%)9.1%
Memory size135.8 KiB
2020-11-19T18:57:46.873769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q348
95-th percentile138.1
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.30503039
Coefficient of variation (CV)1.382013918
Kurtosis7.571001747
Mean35.67621842
Median Absolute Deviation (MAD)16
Skewness2.499236891
Sum620017
Variance2430.986021
MonotocityNot monotonic
2020-11-19T18:57:47.110095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
015819.1%
 
110826.2%
 
27984.6%
 
36974.0%
 
45613.2%
 
55092.9%
 
64482.6%
 
74052.3%
 
83772.2%
 
93482.0%
 
Other values (312)1057360.8%
 
ValueCountFrequency (%) 
015819.1%
 
110826.2%
 
27984.6%
 
36974.0%
 
45613.2%
 
ValueCountFrequency (%) 
3671< 0.1%
 
3621< 0.1%
 
3611< 0.1%
 
3571< 0.1%
 
3561< 0.1%
 

registered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct776
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.7868692
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Memory size135.8 KiB
2020-11-19T18:57:47.287401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q134
median115
Q3220
95-th percentile465
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.3572859
Coefficient of variation (CV)0.9842016207
Kurtosis2.750017757
Mean153.7868692
Median Absolute Deviation (MAD)89
Skewness1.557904226
Sum2672662
Variance22909.028
MonotocityNot monotonic
2020-11-19T18:57:47.425128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
43071.8%
 
32941.7%
 
52871.7%
 
62661.5%
 
22451.4%
 
12011.2%
 
72001.2%
 
81901.1%
 
91781.0%
 
111400.8%
 
Other values (766)1507186.7%
 
ValueCountFrequency (%) 
0240.1%
 
12011.2%
 
22451.4%
 
32941.7%
 
43071.8%
 
ValueCountFrequency (%) 
8861< 0.1%
 
8851< 0.1%
 
8762< 0.1%
 
8711< 0.1%
 
8601< 0.1%
 

cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct869
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.4630876
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Memory size135.8 KiB
2020-11-19T18:57:47.564081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q140
median142
Q3281
95-th percentile563.1
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.3875991
Coefficient of variation (CV)0.9573769823
Kurtosis1.417203281
Mean189.4630876
Median Absolute Deviation (MAD)112
Skewness1.277411604
Sum3292679
Variance32901.4611
MonotocityNot monotonic
2020-11-19T18:57:47.701988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52601.5%
 
62361.4%
 
42311.3%
 
32241.3%
 
22081.2%
 
71981.1%
 
81821.0%
 
11580.9%
 
101550.9%
 
111470.8%
 
Other values (859)1538088.5%
 
ValueCountFrequency (%) 
11580.9%
 
22081.2%
 
32241.3%
 
42311.3%
 
52601.5%
 
ValueCountFrequency (%) 
9771< 0.1%
 
9761< 0.1%
 
9701< 0.1%
 
9681< 0.1%
 
9671< 0.1%
 

Interactions

2020-11-19T18:57:32.107421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.213884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.317492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.428306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.556074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.679702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.789883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:32.895792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.018642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.123699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.229380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.331313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.431347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.536903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.640029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.744733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.851590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:33.952606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.061024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.160567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.262949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.375269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.482627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.594531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.710843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.822512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:34.932501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.043671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.157677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.379866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.506981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.618043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.722930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.833926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:35.944293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.063063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.172297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.279481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.393535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.499336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.606258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.713928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.818692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:36.928503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.037476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.146172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.253510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.358336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.471031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.574860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.681818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.788709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:37.891104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.000927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.110014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.217999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.325305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.430065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.636503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.742183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.848511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:38.953080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.063867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.171366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.276838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.380937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.484321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.586296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.697405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.798598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:39.904404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.020536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.133972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.252842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.367765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.482362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.598493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.712163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.832459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:40.947058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.064718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.171006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.272181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.380001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.486810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.592348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.700803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:41.898830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.010709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.112247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.221127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.331293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.438594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.548872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.658705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.766219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.874649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:42.982959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:43.109892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:43.215202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-19T18:57:47.841009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-19T18:57:48.060145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-19T18:57:48.269410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-19T18:57:48.477315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-19T18:57:48.648418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-19T18:57:43.445412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:43.760703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-19T18:57:43.914629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
0None2011-01-01101006010.240.28790.810.000031316
1None2011-01-01101106010.220.27270.800.000083240
2None2011-01-01101206010.220.27270.800.000052732
3None2011-01-01101306010.240.28790.750.000031013
4None2011-01-01101406010.240.28790.750.0000011
5None2011-01-01101506020.240.25760.750.0896011
6None2011-01-01101606010.220.27270.800.0000202
7None2011-01-01101706010.200.25760.860.0000123
8None2011-01-01101806010.240.28790.750.0000178
9None2011-01-01101906010.320.34850.760.00008614

Last rows

instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
17369None2012-12-3111121401120.280.27270.450.223962185247
17370None2012-12-3111121501120.280.28790.450.134369246315
17371None2012-12-3111121601120.260.25760.480.194030184214
17372None2012-12-3111121701120.260.28790.480.089614150164
17373None2012-12-3111121801120.260.27270.480.134310112122
17374None2012-12-3111121901120.260.25760.600.164211108119
17375None2012-12-3111122001120.260.25760.600.164288189
17376None2012-12-3111122101110.260.25760.600.164278390
17377None2012-12-3111122201110.260.27270.560.1343134861
17378None2012-12-3111122301110.260.27270.650.1343123749